class_docstr: Wraps ``torch.nn.Module`` to overload ``__call__`` instead of
  ``forward`` for better type checking.
  
  `PyTorch Github issue for clarification <https://github.com/pytorch/pytorch/issues/44605>`_
class_docstr_tok: ["Wraps","``","torch.nn.Module","``","to","overload","``","__call__","``","instead","of","``","forward","``","for","better","type","checking",".","`","PyTorch","Github","issue","for","clarification","<","https",":","\/\/github.com\/pytorch\/pytorch\/issues\/44605",">","`","_"]
code: def device(self):
      params = self.parameters()
      try:
          sample_param = next(params)
          return sample_param.device
      except StopIteration:
          raise RuntimeError(
              f"Unable to determine"
              f" device of {self.__class__.__name__}"
          ) from None
  
file_path: labml/helpers/labml_helpers/module.py
imports: from typing import Any, TypeVar, Iterator, Iterable, Generic
  import torch.nn
label_desc: Use image node_id 3 for calling the Module obj's underlying member method code with example usage: obj.device() and returns: sample_param